Abstract
We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model’s outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning.- Anthology ID:
- 2022.aacl-short.2
- Volume:
- Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8–13
- Language:
- URL:
- https://aclanthology.org/2022.aacl-short.2
- DOI:
- Cite (ACL):
- Pinzhen Chen and Zheng Zhao. 2022. A Unified Model for Reverse Dictionary and Definition Modelling. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 8–13, Online only. Association for Computational Linguistics.
- Cite (Informal):
- A Unified Model for Reverse Dictionary and Definition Modelling (Chen & Zhao, AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.aacl-short.2.pdf